Abstract:
This data set consists of portions of three Landsat Thematic Mapper scenes which were analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine land cover analysis and a subsequent change detection analysis. The data were field validated and mosaicked to produce a land cover inventory for Northern Maine. If you wish to create an attribute table to identify the different land cover change classifications, an easy reference table is available under the PROCESS DESCRIPTION section.

Purpose:
To improve the understanding of coastal uplands and wetlands, and their linkages with the distribution, abundance, and health of living marine resources.

Quality
ATTRIBUTE ACCURACY REPORT: A team of field investigators participated in data verification exercises on UNKNOWN. Data validation teams consisted of personnel from the Data validation teams consisted of personnel from the Gulf of Maine Program, the U.S. Fish and Wildlife Service, Environment Canada, Moosehorn Wildlife Refuge, Georgia Pacific, the Oak ... Ridge National Laboratory, and the NOAA Coastal Services Center. Each team was equipped with a portable color laptop computer linked to a Global Positioning System (GPS). The field station runs software that supports the classified data as a raster background with the road network as a vector overlay with a simultaneous display of live GPS coordinates. Accuracy assessment points were generated with ERDAS Imagine software using a stratified random sample in 3x3 pixel homogeneous windows. To make the acquisition of the field reference data more practical, a twenty pixel buffer area around roads (i.e. 10 pixels on each side of the road) was created. 668 random points were generated within this area for the accuracy assessment. Collecting 'ground truth' information for areas that have experienced a change in land cover type is a troublesome task. Pre-Processing Steps: The scene was georectified to UTM Zone 19 coordinates. Ancillary data sets: Subsequent field work and the use of collateral data such as USGS maps, county marsh inventories, and National Wetland Inventory data led to further refinements in the image classification. Shoreline features can be extracted from Landsat images by detecting the land/water interface. However, care must be used to avoid misinterpreting tidal differences as changes in shorelines, since the satellite images from which these land cover images are derived and acquired at different tidal stages, depending on when the satellite is overhead. The land cover classifications represent the instantaneous state of the shoreline at the moment of image acquisition. C-CAP data are mapped at 1:100,000 scale with 22 standard classes constituting major landscape components. They are not jurisdictional (can't be used for permitting) and will not identify individual species. However, they are useful for identifying regional landscape patterns, major functional niches, environmental impact assessment, urban planning, and zoning applications. If you need change analysis data at this scale, C-CAP may be your only option. C-CAP is designed around a 1 to 5 year revisit cycle. Land Cover is the complete human and natural landscape recorded as surface components - forest, water, wetlands, concrete, asphalt, etc. Land cover can be documented by analyzing spectral signatures of satellite and aerial imagery. Land Use is the documentation of human uses of the landscape - residential, commercial, agricultural, etc. Land use can be inferred but, not explicitly derived from satellite and aerial imagery. There is no spectral basis for land use determination in satellite imagery. C-CAP data can be used to identify concrete and asphalt as land cover, but we can only infer that these materials denote a residential or commercial use. Post-Processing Steps: The data were put through a datum conversion from UTM NAD27 to UTM NAD83 zone 19. Known Problems: None Accuracy Results: None performed, therefore unknown, refer to Use Constraints for more information.

LOGICAL CONSISTENCY REPORT: Tests for logical consistency indicate that all row and column positions in the selected latitude/longitude window contain data. Conversion and integration with vector files indicates that all positions are consistent with earth coordinates covering the same area. Attribute files appear to be logically consistent. Examining the change matrix for logical fallacies, we find, for example, that a very small number of pixels changed from developed land to any other category.

COMPLETENESS REPORT: The classification scheme comprehensively includes all anticipated land covers, and all pixels have been classified. The NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation, NOAA National Marine Fisheries Service Report 123, discusses the interagency effort to develop the land cover classification scheme and defines all categories.

VERTICAL POSITIONAL ACCURACY REPORT: There was no terrain correction in the georeferencing procedure.

PROCESS DESCRIPTION: The processing steps for each C-CAP Northern Maine Land Cover Change Analysis 1985 - 1992 product are intricately associated. Each database is the result of many processing steps with numerous iterations for each step. The output of one processing step or database is often the input data for another processing step. A brief description of the processing steps used in the land cover classification of the this project follows. Further description of the processing steps can be found in the NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation, NOAA National Marine Fisheries Service Report 123 (Dobson et al, 1995). Baseline Classification Process: The Northern Maine land cover/change classification product was processed using an iterative classification approach. Landsat Thematic Mapper data for path/row/date(s): 10/29 19850523, 10/29 19920526 were analyzed and mosaicked to create a land cover inventory for Northern Maine. Each scene was classified, focusing first on separating major categories (e.g. water, forest, marsh, herbaceous upland, and developed) using standard supervised classification techniques. Numerous individual areas were chosen as training sites for the land cover classification. The mean and covariance statistics for these areas are passed to an isodata classification algorithm which assigns an unknown pixel to the class in which it has the highest probability of being a member. Then iterative unsupervised classifications were performed on each major category individually by masking out all other major categories. With this type of unsupervised classification, the computer is allowed to query the multispectral properties of the masked scene using user specified criteria and to identify X mutually exclusive clusters in N-dimensional feature space. By masking out all data but a single major category, the spectral variance is greatly reduced thus decreasing classification errors. After several classification iterations of the masked data, final classification labels were assigned to the spectral clusters. Changes among major categories were permitted to occur even at this stage of processing. Subsequent field work and the use of collateral data such as USGS maps, TIGER road data, and National Wetland Inventory data led to further refinements in the image classification. In small areas where landcover class confusion could not be separated spectrally, human pattern recognition was used to recode the data. A spatial filter was applied to the final classification data file. Change Classification Process: Landsat Thematic Mapper data for path/row(s) 10/29 19850523, 10/29 19920526 were analyzed to arrive at a land cover for Northern Maine. The change date land cover classification was in part derived from the baseline classification. Only the pixels in the May 26, 1992 image that changed spectrally from the change date image were classified for the May 23, 1985 data file. All other pixels were simply replaced with the baseline image classification. It is possible to simply identify the amount of change between two images by image differencing the same band in two images which have previously been rectified to a common basemap. Image differencing involves subtracting the imagery of one date from that of another. The subtraction results in positive and negative values in areas of radiance change and zero values in areas of no-change in a new 'change image'. The images are subtracted resulting in an signed 16-bit analysis with pixel values ranging from -255 to 255. The results were transformed into positive unsigned 16-bit values by adding a constant, c. The operation is expressed mathematically as: Dijk = BVijk(1) - BVijk(2) + c where Dijk = change pixel value BVijk(1) = brightness value at time 1 BVijk(2) = brightness value at time 2 c = a constant (e.g., 255). i = line number j = column number k = a single band (e.g. TM band 4). The 'change image' produced using image differencing usually yields a BV distribution approximately gaussian in nature, where pixels of no BV change are distributed around the mean and pixels of change are found in the tails of the distribution. A threshold value was carefully chosen to identify spectral 'change' and 'no-change' pixels in the 'change image.' A 'change/no-change' mask was derived by performing image differencing on band 4, and Normalized Difference Vegetation Index (NDVI) of the two date dataset and recoded into a binary mask file. The 'change/no-change' mask was then overlaid onto the earlier date of imagery and only those pixels which were detected as having spectrally changed were viewed as candidate pixels for categorical change. Change Detection Database The change date and baseline land cover classifications were compared on a pixel by pixel basis using a change detection matrix. This traditional post-classification comparison yields 'from land cover class - to land cover class' change information. Many pixels with sufficient change to be included in the mask of candidate pixels in the spectral change process did not qualify as categorical land cover change. This method may reduce change detection errors (omission and commission) and provides detailed 'from-to' change class information. The technique reduces effort by allowing analysts to focus on the small amount of area that has changed between dates. PROCESS DATE: 19940930

Access Constraints
ACCESS CONSTRAINTS: None, except for a possible fee at the cost of reproduction.

DISTRIBUTION LIABILITY: Users must assume responsibility to determine the usability of these data.

Use Constraints
Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, NOAA, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty. Additional Use Constraints: This data set was the second C-CAP Analysis completed and still considered a prototype. No accuracy assessment was performed on this data set and no accuracy assessment statistics are available.